Posts from April 2013 (67)

Team Ratings for Round 10

This year the predictions have been slightly changed with the help of a student, Joshua Dale. The home ground advantage now is different when both teams are from the same country to when the teams are from different countries. The basic method is described on my Department home page.

Here are the team ratings prior to Round 10, along with the ratings at the start of the season.

Performance So Far

So far there have been 55 matches played, 38 of which were correctly predicted, a success rate of 69.1%.

Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 10. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Stuff has a story “Dishonest Kiwi Travellers”, based on a survey press release from Hotels.com.  The survey asked people if they had stolen things from hotels and used the responses to rank countries by honesty, with the travellers who denied taking things being rated more honest than the ones who admitted it (rather than the other way around).

Fortunately it doesn’t really matter how honest the responses were, since if you follow a few links you can find a press release for the Canadian part of the survey, which admits

In a recent survey hotels.com® asked its Canadian email subscribers , including those in Quebec, about what they look for in hotel accommodations, and you might be surprised at what they had to say.

Or in other words, it’s a bogus poll.

• Martin Johnston in the Herald on a nationwide blood pressure survey. Blood pressures are up.  This is not good.
• Nikki Macdonald in Stuff, on recreational genotyping (or as the story more properly calls it, “direct to consumer” genotyping).

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday April 19 2013.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 13 – 19 2013 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

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If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

Today we have two examples of the important issue of communicating scientific knowledge about infectious disease epidemics.

The first is the WHO, which is doing an excellent job of describing the limited information about the new H7N9 influenza outbreak in China. Their media release is here, and they’ve had someone answering questions on Twitter as well as more traditional venues.  There’s currently evidence of a small amount of human-to-human transmission, but not enough to sustain a pandemic. On the other hand, the virus does appear to have mutated to live more successfully in people, and this could continue.  They don’t advise actually doing anything specific at the moment.

The second is the UK measles epidemic, where The Independent, has as its top front-page headline “MMR scare doctor: this outbreak proves I was right”.  Of course,  it does nothing of the sort, as the story admits later . He’s claiming that the MMR vaccine should have been replaced by three single vaccines, and even if you believe that anti-vaccination campaigners would then suddenly have stopped their misrepresentations, having three single vaccinations is actually more dangerous than one combined one.

The Independent is maintaining that its story is accurate if you read the whole thing. Even if that were so, it’s still hard to imagine why the opinion of a discredited researcher and struck-off former doctor is the single most important piece of information they have about epidemic and the world today. And, as  Martin Robins writes in New Statesman

 It would be a great example of the false balance inherent in ‘he-said, she-said’ reporting, except that it isn’t even balanced – Laurance provides a generous abundance of space for Wakefield to get his claims and conspiracy theories across, and appends a brief response from a real scientist at the end. 

• Skepticism about ‘big data’ at O’Reilly Radar
• How data goes wrong from Tomorrow Global.  Along the same lines, the famous story from Howard Cox

“Cox, when you are a bit older, you will not quote Indian statistics with that assurance. The Government are very keen on amassing statistics – they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But what you must never forget is that every one of these figures comes in the first place from the  [village watchman], who just puts down what he damn pleases”

• The Economist has an international currency index based on the price of  a Big Mac.  At Quartz, an alternative index using the price of the Economist, which suggests the NZ$ is undervalued.

Here’s a fun link which talks about the difference between truly random numbers and pseudo-random numbers. When we teach this, we often mention generation of random numbers (or at least the random number seed) from a radioactive source as one way of getting truly random numbers. Here is someone actually doing it. The sequel is well worth a watch too if you have the time.

If you have a large collection of measurements there isn’t going to be a unique way to put them together into a single ordering: what you get out depends to some extent on your criteria. That doesn’t mean the measurements, or even the rankings, are meaningless, but it does mean that you should work out what your criteria are before you see the data, and that criticism of the choice of criteria is perfectly reasonable.

An illustration is yesterday’s PBRF research evaluation results.  For those of you playing along at home, PBRF is one of the mechanisms the country uses to allocate research funding. Unlike individual research grants, which are based on competition between individual proposals, PBRF funding is allocated to large groups of researchers for long periods of time based on a aggregated results from a single standardised evaluation.

Though it’s not the point of the system, the existence of a large number of grades invariably tempts university management into coming up with ways to combine them to make their institution look good. And they always succeed:

• In first place, we have the University of Auckland: “secured the largest share of the fund, $80.4m or 30.6% of the national total… This is due in part to the University’s impressive 288 international quality (A-rated) researchers – the greatest number of leading researchers anywhere in the country.”
• And in first place, the University of Otago: “Otago was ranked first among New Zealand universities in the measure of research quality weighted by its postgraduate roll (AQS (P)) and second in the measure weighted by degree-level enrolments and higher (AQS (E)). The University is the only TEO to be ranked in the top four in all four AQS measures.”
• In first place, also, Victoria University of Wellington: ” the latest PBRF Evaluation ranks Victoria as number one in New Zealand….With 678 staff actively involved in research, and 70 percent of them operating at the highest levels (ranked as either an A or B), we now have external confirmation of our status as New Zealand’s most research intensive university.”
• And finally, in first place (special subject), Lincoln University:  “confirms Lincoln University’s position as New Zealand’s specialist land-based university. … Lincoln University also has the highest amount of external funding for research (measured as income/staff member), demonstrating close links with industry and relevance of the University’s research.”

Other institutions aren’t claiming first place, but are still saying that the PBRF demonstrate how successful they have been. This has been another illustration that anyone saying “the data speak for themselves” is not to be trusted. The question matters.

• “With less than two months to go until I graduate from the UC Berkeley Graduate School of Journalism, I’ve been looking back at my experience over the past two years. I’m among a handful of students at the school who are really interested in data journalism and making pretty and functional online news packages. It’s made me think about how J-schools need a more structured and thorough track for us computer-assisted reporters, for lack of a better term.” John Osborn

• “Send me your data — PDF is fine” said no one ever. Caitlin Rivers, via @adzebill

• Post-mortem on a cancer diagnostics company: academic research suffers from publication bias and hype too.